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化学 高校生

模試の高三化学溶解平衡の問題です。 二番の問題が解説ではそれぞれが沈殿しているのか仮定してその上でモル濃度を調べてそれを比で計算すると言った流れなのですが、Ksp(CuS)/Ksp(ZnS)をしても同じ比にならないのは、硫化亜鉛が沈殿していないからですか❓もし仮にどちらも沈... 続きを読む

問8 CuSO4 と ZnSO4 をともに1.0×10 -3 mol/Lのモル濃度で含む混合水溶液 (水溶 液S とする)に,pH を 3.0 に保ちながら硫化水素 HS を十分に通じた。これにつ 下の(1),(2)に答えよ。 なお、H2Sは水に溶けると以下の式 (i), (i) のよう に二段階で電離し,式 (i) の電離定数はK=1.0×10-7 mol/L, 式 (i) の電離定数 は K2 = 1.0×10-14 mol/L とする。 H2S ← H+ + HS'] [H+] [HS] ...(i) Ki= [H2S] HS¯⇌ H+ + S2- [H+][S2-] (ii) K2= [HS] また, H2Sを十分に通じて飽和させた水溶液中では, pHによらず [H2S] = 0.10 mol/Lとし, H2Sを通じたり沈殿が生成したりしても、水溶液の体積は変化しな いものとする。 (1)pHを3.0に保った水溶液にH2Sを十分に通じたとき,S2のモル濃度 [S2-] は何 mol/L になるか。四捨五入により有効数字2桁で記せ。1.0×10-16 (2)水溶液S に, pH を 3.0 に保ちながらHSを十分に通じた後の水溶液に存在 [Cu を四捨五入により有効数字2桁 する Cu2+ と Zn2+のモル濃度の比 で記せ。 ただし, CuSとZnSの溶解度積 Ksp(Cus), Ksp(Zns) はそれぞれ次のと [Zn2+] おりとする。 3.0 10-12 3.0×10-12 CuS:Ksp(cus)= [Cu2+][S2]=6.3×100(mol/L)2 ZnS:Ksp(zns)=[Zn2+[S2-]=21×10-M (mol/L)2 5 なんで つけ 星 2 天

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英語 中学生

中3 英語 長文読解 問1 本文の内容から考えて、とありますがどこで判断して分かりますか?答えはウです

5 次の英文は, ジュディ (Judy) と健 (Ken) がボランティア活動をする (do volunteer work) ことについて対話しているもので す。 これを読んで問いに答えなさい。 Judy Ken, do you know children in the U.S. often do volunteer work? Ken: Oh, really? I didn't know about that. I'm interested in it, but I haven't ( ①) volunteer work yet. I want to know (1) Judy Well, in the U.S. they usually clean the parks and beaches, help at *animal shelters, or even read books to *elderly people in *nursing homes. Ken Great! I think we can do the same things in Japan. Judy: You're right. My mother and I like the animals at the zoo. A few years ago, we did volunteer work at the local zoo in my town. Ken: Really? I've never heard of ( 2 ) volunteer work at the zoo. What did you do there? Judy We helped to *prepare food for the animals and clean their *cages. Ken: I think you ( 3 ) a lot for the zoo. Judy: Yes. We also learned a lot about the animals. Ken: I think that was a good experience. I also like animals. So, I want to do some useful volunteer work at the zoo someday. Judy, in Japan, I've heard that *volunteers (help) local people after some of the serious *earthquakes since 2011. I think getting help from volunteers is a great help to them. Judy: Yes. (ways/to/are/many/help/there) other people. Ken: That's right. Oh, I remembered our *neighborhood association will have a cleaning day next weekend. I'll join it. Judy That's a good idea. We don't have such an event in U.S. Can I join you? Ken: Sure! We will start at 10 in the morning. Let's meet at Midori Park. Please bring some *work gloves. Judy : I see. ! (注) animal shelter (s) ・・・ 動物保護施設 ... elderly ... お年寄りの おり cage (s) volunteer (s) nursing homes ・・・ 高齢者福祉施設 ボランティアをする人 ... work gloves PF prepare... 〜の準備をする earthquake (s) ... neighborhood association J 問1 本文の内容から考えて,(1) (③)に入る語の組み合わせとして最も適当なものを, ア~エから選びなさい。 ア (1) do (2) did (3) done ウ (1) done 2 doing (3) did イ (1) did (2) done 3 doing I 1 doing (2) did done

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TOEIC・英語 大学生・専門学校生・社会人

この長文問題の答えと解説をお願いします。

15 語数: 398 語 出題校 法政大 5 We are already aware that our every move online is tracked and analyzed. But you 2-53 couldn't have known how much Facebook can learn about you from the smallest of social interactions - a 'like'*. (1) Researchers from the University of Cambridge designed (2) a simple machine-learning 2-54 system to predict Facebook users' personal information based solely on which pages they had liked. E "We were completely surprised by the accuracy of the predictions," says Michael 2-55 Kosinski, lead researcher of the project. Kosinski and colleagues built the system by scanning likes for a sample of 58,000 volunteers, and matching them up with other 10 profile details such as age, gender, and relationship status. They also matched up those likes with the results of personality and intelligence tests the volunteers had taken. The team then used their model to make predictions about other volunteers, based solely on their likes. The system can distinguish between the profiles of black and white Facebook users, 15 getting it right 95 percent of the time. It was also 90 percent accurate in separating males and females, Democrats and Republicans. Personality traits like openness and intelligence were also estimated based on likes, and were as accurate in some areas as a standard personality test designed for the task. Mixing what a user likes with many kinds of other data from their real-life activities could improve these predictions even more. 20 Voting records, utility bills and marriage records are already being added to Facebook's database, where they are easier to analyze. Facebook recently partnered with offline data companies, which all collect this kind of information. This move will allow even deeper insights into the behavior of the web users. 25 30 (3) - Sarah Downey, a lawyer and analyst with a privacy technology company, foresees insurers using the information gained by Facebook to help them identify risky customers, and perhaps charge them with higher fees. But there are potential benefits for users, too. Kosinski suggests that Facebook could end up as an online locker for your personal information, releasing your profiles at your command to help you with career planning. Downey says the research is the first solid example of the kinds of insights that can be made through Facebook. "This study is a great example of how the little things you do online show so much about you,” she says. "You might not remember liking things, " but Facebook remembers and (4) it all adds up.", * a 'like': フェイスブック上で個人の好みを表示する機能。 日本語版のフェイスブックでは「いいね!」 と表記される。 2-56 2-57 2-58 36

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